Extending the Technology Acceptance Model to Consumer Perceptions of Fashion AI

This research intends to investigate consumers\u27 acceptance and purchase intention towards a fashion AI device so as to predict how consumers\u27 fashion sense will be affected by new technologies. The extended Technology Acceptance Model (TAM) was used as theoretical framework, along with performance risk and positive technology attitudes. Empirical data (with 313 valid responses) were collected from top 10 metropolitan areas in the US via Qualtrics Panel services. Structural equation modeling and multiple group analysis were used to estimate construct validity and test the proposed hypotheses and theoretical framework. Results indicated that consumers’ acceptance and purchase intention were predicted by favorable attitudes toward the fashion AI device and positive technology attitude. Usefulness, ease of use, enjoyment, and performance risk significantly influence customers’ attitudes. Consumers of different levels of fashion involvement have various purchase intention. Theoretical and practical implications were presented

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

EFFECTS OF APPROPRIATE AND INAPPROPRIATE ATTIRE ON ATTRIBUTIONS OF PERSONAL DISPOSITIONS

In an effort to ascertain whether clothing cues are used in the manner predicted by correspondent inference theory, subjects were exposed to one of two videotaped interviews. In a complete factorial design, the interview was described as one for a campus position as either (1) an orientation adviser or (2) a groundskeeper. The interviewee was dressed in either (1) a pair of overalls with a plaid shirt or (2) a skirted suit. It was predicted that attributions of personal dispositions would be more extreme and given with more confidence when the interviewee was inappropriately dressed for the interview than when she was appropriately dressed. Contrary to prediction, there was no interaction between job and clothing on extremity of trait ratings. There were two inappropriate cells: groundskeeper-suit and orientation adviser--overalls. Neither received significantly more extreme ratings than the appropriate cells. There was an interaction between job and clothing on confidence ratings but it was not in the direction predicted. The inappropriate cell, where the interviewee was dressed in overalls for the orientation adviser job interview, yielded significantly less confident ratings, rather than more, as predicted. Post hoc analyses of the manipulation check items suggested that subjects appeared to seek and find a reason for the inappropriate attire in both the inappropriate cells. In the case of the groundskeeper interviewee dressed in a suit, subjects judged her as having had less choice and by implication as reacting to situational constratints...perhaps to impress the interviewer and thus increase the chances of being hired (motivation was perceived as high). In the case of the orientation adviser interviewee dressed in overalls, subjects judged her as less interested in being hired for the job (implying a lack of exertion). But, apparently, ambiguity surrounding this lack of interest resulted in subjects being less confident in their evaluations of the interviewee. Heider suggested that under conditions of situational constraint (lack of choice) and low effort (lack of trying), little information about personal dispositions can be gained. Subjects in the present study apparently acted in accord with Heider\u27s position, as they gave modal responses and avoided extreme ratings

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press